Multicriteria Based Recommender System for Enhancing Customer Centric Business

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Nivetha M ◽  
A. Meenakowshalya
Keyword(s):  
Recommender System ◽  
Life Insurance ◽  
Optimal Solution ◽  
Fuzzy Topsis ◽  
User Preferences ◽  
Insurance Plan ◽  
Fuzzy Vikor ◽  
Topsis Approach ◽  
Ranking Order ◽  
The Stability

A multi-criteria-based recommender system makes precise decisions by probing customers multiple criteria’s on-hand and gives recommendations by modelling a user’s utility for the alternatives along several criteria. This paper collects user preferences of criteria over alternatives in the form of linguistic variables. Alternatives includes 6 policies namely term plan, endowment plan, child plan, life insurance plan, criteria include income tax benefit, sum assured, benefits on death and riders option. To rank such alternatives, fuzzy vikor expanded form of MCDM (MCDM) technique is used. MCDM ranks policies based on expand S (S) and expand R (R) value. Sensitivity analysis is used to determine the stability in the alternatives ranking, with the varying parameter value V. The proposed approach is compared with fuzzy topsis approach which ranks alternatives based on closeness coefficient value obtained. After ordering the alternatives using the MCDM techniques, it is inferred that, Both the approach almost provides the closest ranking order. The proposed fuzzy vikor provides best and optimal solution preserving the consistency compared to fuzzy topsis.

scholarly journals Movie Recommendation through Multiple Bias Analysis

2021 ◽  
Vol 11 (6) ◽  
pp. 2817
Author(s):  
Tae-Gyu Hwang ◽  
Sung Kwon Kim
Keyword(s):  
Decision Making ◽  
Recommender System ◽  
Real World ◽  
Matrix Factorization ◽  
User Preferences ◽  
Improve Performance ◽  
Bias Analysis ◽  
Useful Knowledge ◽  
Existing Problems ◽  
Movie Recommendation

A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization (MF). Therefore, a new method is required for analyzing preference patterns, which could not be derived by existing studies. This study aimed at solving the existing problems through bias analysis. By analyzing users’ and items’ biases of user preferences, the bias-based predictor (BBP) was developed and shown to outperform memory-based CF. In this paper, in order to enhance BBP, multiple bias analysis (MBA) was proposed to efficiently reflect the decision-making in real world. The experimental results using movie data revealed that MBA enhanced BBP accuracy, and that the hybrid models outperformed MF and SVD++. Based on this result, MBA is expected to improve performance when used as a system in related studies and provide useful knowledge in any areas that need features that can represent users.

scholarly journals Accuracy improvements for cold-start recommendation problem using indirect relations in social networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Fu Jie Tey ◽  
Tin-Yu Wu ◽  
Chiao-Ling Lin ◽  
Jiann-Liang Chen
Keyword(s):  
Social Media ◽  
Recommender System ◽  
Mobile Networks ◽  
Reference Value ◽  
User Preferences ◽  
Digital Information ◽  
Internet Applications ◽  
Social Media Networks ◽  
Recommendation Accuracy ◽  
Gain Access

AbstractRecent advances in Internet applications have facilitated information spreading and, thanks to a wide variety of mobile devices and the burgeoning 5G networks, users easily and quickly gain access to information. Great amounts of digital information moreover have contributed to the emergence of recommender systems that help to filter information. When the rise of mobile networks has pushed forward the growth of social media networks and users get used to posting whatever they do and wherever they visit on the Web, such quick social media updates already make it difficult for users to find historical data. For this reason, this paper presents a social network-based recommender system. Our purpose is to build a user-centered recommender system to exclude the products that users are disinterested in according to user preferences and their friends' shopping experiences so as to make recommendations effective. Since there might be no corresponding reference value for new products or services, we use indirect relations between friends and “friends’ friends” as well as sentinel friends to improve the recommendation accuracy. The simulation result has proven that our proposed mechanism is efficient in enhancing recommendation accuracy.

Modelling trust networks using resistive circuits for trust-aware recommender systems

2016 ◽  
Vol 43 (1) ◽  
pp. 135-144 ◽  
Author(s):  
Mehdi Hosseinzadeh Aghdam ◽  
Morteza Analoui ◽  
Peyman Kabiri
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cold Start ◽  
User Preferences ◽  
Item Selection ◽  
Trust Networks ◽  
Trust Inference ◽  
Trust Information

Recommender systems have been widely used for predicting unknown ratings. Collaborative filtering as a recommendation technique uses known ratings for predicting user preferences in the item selection. However, current collaborative filtering methods cannot distinguish malicious users from unknown users. Also, they have serious drawbacks in generating ratings for cold-start users. Trust networks among recommender systems have been proved beneficial to improve the quality and number of predictions. This paper proposes an improved trust-aware recommender system that uses resistive circuits for trust inference. This method uses trust information to produce personalized recommendations. The result of evaluating the proposed method on Epinions dataset shows that this method can significantly improve the accuracy of recommender systems while not reducing the coverage of recommender systems.

Corporate sustainability assessment through fuzzy TOPSIS

2018 ◽  
Author(s):  
Eleonora Bottani ◽  
Marta Rinaldi ◽  
Federico Solari
Keyword(s):  
Sustainability Assessment ◽  
Corporate Sustainability ◽  
Reference Value ◽  
Fuzzy Topsis ◽  
Analysis And Evaluation ◽  
Sustainability Performance ◽  
Order Preference ◽  
Pros And Cons ◽  
Topsis Approach ◽  
Corporate Level

"The aim of this paper is to propose a decisionmaking methodology that enables the analysis and evaluation of sustainability at the corporate level. The proposed methodology grounds on two tools, namely the technique for order preference by similarity to ideal solution (TOPSIS) approach and fuzzy logic. The integration of these tools offers an effective way to deal with two typical issues of sustainability assessment, i.e.: 1) the fact that the company’s performance should be frequently evaluated against qualitative key performance indicators; and 2) the fact that to be meaningful, the company’s sustainability performance needs to be compared to a reference value, e.g. a threshold or benchmark, to evaluating how the company is distant from a target. The proposed approach has been applied to a real firm, operating in the food machinery industry, for testing purpose. The main pros and cons of the approach are described."

scholarly journals Managing cost uncertainties in transportation and assignment problems

1998 ◽  
Vol 2 (1) ◽  
pp. 65-104 ◽  
Author(s):  
V. Adlakha ◽  
H. Arsham
Keyword(s):  
Time Lag ◽  
Real Life ◽  
Optimal Solution ◽  
Global Market ◽  
Solution Algorithm ◽  
Assignment Problems ◽  
The Stability ◽  
Order Assignment ◽  
Cost Parameters ◽  
The Cost

In a fast changing global market, a manager is concerned with cost uncertainties of the cost matrix in transportation problems (TP) and assignment problems (AP).A time lag between the development and application of the model could cause cost parameters to assume different values when an optimal assignment is implemented. The manager might wish to determine the responsiveness of the current optimal solution to such uncertainties. A desirable tool is to construct a perturbation set (PS) of cost coeffcients which ensures the stability of an optimal solution under such uncertainties.The widely-used methods of solving the TP and AP are the stepping-stone (SS) method and the Hungarian method, respectively. Both methods fail to provide direct information to construct the needed PS. An added difficulty is that these problems might be highly pivotal degenerate. Therefore, the sensitivity results obtained via the available linear programming (LP) software might be misleading.We propose a unified pivotal solution algorithm for both TP and AP. The algorithm is free of pivotal degeneracy, which may cause cycling, and does not require any extra variables such as slack, surplus, or artificial variables used in dual and primal simplex. The algorithm permits higher-order assignment problems and side-constraints. Computational results comparing the proposed algorithm to the closely-related pivotal solution algorithm, the simplex, via the widely-used pack-age Lindo, are provided. The proposed algorithm has the advantage of being computationally practical, being easy to understand, and providing useful information for managers. The results empower the manager to assess and monitor various types of cost uncertainties encountered in real-life situations. Some illustrative numerical examples are also presented.

scholarly journals Prioritizing the Solutions to Reverse Logistics Barriers for the E-Commerce Industry in Pakistan Based on a Fuzzy AHP-TOPSIS Approach

2021 ◽  
Vol 13 (22) ◽  
pp. 12743
Author(s):  
Muhammad Hamza Naseem ◽  
Jiaqi Yang ◽  
Ziquan Xiang
Keyword(s):  
Supply Chain ◽  
Literature Review ◽  
Reverse Logistics ◽  
Fuzzy Ahp ◽  
Pairwise Comparison ◽  
Fuzzy Topsis ◽  
Extensive Literature ◽  
Forward Logistics ◽  
Reverse Logistics Management ◽  
Topsis Approach

In the past few years, reverse logistics practices have successfully managed to gain more attention in various industries and among supply chain researchers and experts. This is due to globalization, environmental concerns, and customer requirements, which have asserted industries’ concerns for reverse logistics management. In E-commerce, the process of reverse logistics originates with parcel refusal, undelivered goods, and exchanges. In developing countries like Pakistan, the adoption and implications of reverse logistics are still at their early stages. E-commerce companies give more attention to forward logistics and ignore logistics’ upstream flow in the supply chain. This study aims to identify, as well as list, the barriers and obtain the solutions to those identified barriers, and rank the barriers and their solutions so that logisticians and experts can solve them as per their priority. From the extensive literature review and experts’ opinions, we have found 14 barriers in implementing effective reverse logistics. Eight solutions to those barriers were also found from the literature review. This paper proposed the methodology based on fuzzy analytical hierarchy process (fuzzy-AHP), which used to get the weights of each barrier by using pairwise comparison, and fuzzy technique for order performance by similarity to ideal solution (fuzzy-TOPSIS) method, which was adopted for the final ranking of solutions to reverse logistics. The case of the Pakistan E-commerce industry is used in the proposed method.

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